S72 Revolutionizing Biliary Diagnostics: The Role of AI in Enhancing Choledocholithiasis Diagnosis—A Systematic Review and Meta-Analysis

Introduction: Accurate diagnosis of choledocholithiasis (CDL) is essential to avoid delayed treatment, prevent complications, and reduce unnecessary interventions. Traditional guidelines, such as those from the American Society for Gastrointestinal Endoscopy, aid in risk stratification but may lack precision. Artificial intelligence (AI) and machine learning offer emerging diagnostic tools that may enhance the accuracy and timeliness of CDL prediction. We aimed to systematically evaluate the diagnostic performance of AI-assisted tools in predicting CDL and compare it to traditional guideline-based methods. Methods: A comprehensive search was conducted in MEDLINE, EMBASE, PubMed, and Web of Science, identifying 578 studies. After screening and application of inclusion criteria, 11 studies were analyzed. A bivariate random-effects model was used to pool sensitivity, specificity, and positive likelihood ratios. Summary receiver operating characteristics curves were also generated. Results: Meta-analysis showed an overall high pool sensitivity and specificity of AI-assisted models were 83.2% [95% confidence interval (CI): 68.9; 91.8] and 91.1% [95% CI: 84.7; 95], respectively. The likelihood ratios from the common effect model was 8.39 [7.41; 9.50], suggesting that AI models have a moderately strong ability to predict CDL. AI models demonstrated higher diagnostic performance than traditional American Society for Gastrointestinal Endoscopy guidelines, as evidenced by summary receiver operating characteristics comparisons. Conclusion: AI-assisted tools show promise in enhancing CDL diagnosis through high sensitivity and specificity. While preliminary results are encouraging, further prospective studies are necessary to validate these tools and facilitate integration into routine clinical practice.